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Jason Wei

AI researcher at Meta Superintelligence Labs; first author of the chain-of-thought prompting paper

Jason Wei works at Meta Superintelligence Labs; from 2023 to 2025 Wei was at OpenAI, working on reasoning and agents, and before that Wei was a research scientist at Google Brain. Wei is first author of Chain-of-Thought Prompting Elicits Reasoning in Large Language Models and of Finetuned Language Models Are Zero-Shot Learners, which introduced the instruction-tuned FLAN model, and a co-author of Scaling Instruction-Finetuned Language Models. Wei's homepage also lists a 2024 paper on measuring short-form factuality in large language models and a 2025 benchmark for browsing agents.1234

Work

  • Chain-of-thought prompting (external site: arxiv.org)Published research

    First author of the 2022 paper showing that providing a few chain-of-thought demonstrations as exemplars improves large language models on arithmetic, commonsense, and symbolic reasoning tasks.2

  • FLAN instruction tuning (external site: arxiv.org)Published research

    First author of Finetuned Language Models Are Zero-Shot Learners (2021), which instruction-tunes a 137B parameter model on over 60 NLP tasks and evaluates the resulting FLAN model on unseen task types; also a co-author of the 2022 follow-up on scaling instruction finetuning.34

Other documented roles

Roles the sources document but that are not confirmed as current.

This profile covers public professional work only, from the sources below. It is not an endorsement or a ranking. To correct or remove it, use the correction form.

Sources · reviewed Oct 13, 2026

  1. 1.
    Jason Wei — homepage (external site: jasonwei.net)

    Jason Wei · Official page · accessed Oct 13, 2026

  2. 2.
  3. 3.
    Finetuned Language Models Are Zero-Shot Learners (arXiv 2109.01652) (external site: arxiv.org)

    arXiv · Paper · published Sep 3, 2021 · accessed Oct 13, 2026

  4. 4.
    Scaling Instruction-Finetuned Language Models (arXiv 2210.11416) (external site: arxiv.org)

    arXiv · Paper · published Oct 20, 2022 · accessed Oct 13, 2026

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